Optimize NOx Emissions Model of Boiler Based on Chaos Group Teaching-learning-based Optimization Algorithm
MA Yun-peng1,NIU Pei-feng1,CHEN Ke1,YAN Shan-shan2,LI Guo-qiang1
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;
2. Hydropower Station of Administration of Taolinkou Reservoir of Hebei Province, Qinhuangdao, Hebei 066004, China
Abstract：In order to balance the global and local search ability of the teaching-learning-based optimization, an enhanced teaching-learning-based optimization algorithm is proposed. The proposed algorithm adopts three major adjustment mechanisms. First, the chaos method is applied to initialize population. Second, a self-adaptive inertia weight is adopted to update the individual in teaching phase. Finally, in learning phase, the idea of shuffled leap frog algorithm is applied to group the individuals for updating the worst solution. Ten famous testing functions are applied to test the performance of the proposed algorithm, comparing with ABC, GSA and conventional TLBO. The experiment results show that the improved algorithm own better global and local search ability and high convergence precision. Simultaneously, the proposed algorithm is used to optimize the NOx emissions model of circulating fluidized bed boiler, the experiment result shows that the proposed algorithm possesse better identification ability and generalization, and it also can guide the practical project.
［1］Kennedy J. Particle swarm optimization ［M］//Claude Sammut, Geoffrey I Webb. Encyclopedia of Machine Learning. New York: Springer US, 2010: 760-766.
［2］Dorigo M , Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents ［J］. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 1996, 26(1): 29-41.
［3］Yang X S. Firefly algorithm, stochastic test functions and design optimization ［J］. International Journal of Bio-Inspired Computation, 2010, 2(2): 78-84.
［4］Eusuff M, Lansey K, Pasha F. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization ［J］. Engineering Optimization, 2006, 38(2): 129-154.
［5］Karaboga D, Basturk B. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems ［M］ // Patricia Melin , Oscar Castillo , Luis T Aguilar, et al.
Foundations of Fuzzy Logic and Soft Computing. Heidelberg : Springer Berlin Heidelberg , 2007: 789-798.
［5］Yang X S, Deb S. Engineering optimisation by cuckoo search ［J］. International Journal of Mathematical Modelling and Numerical Optimisation, 2010, 1(4): 330-343.
［6］Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems ［J］. Computer-Aided Design, 2011, 43(3): 303-315.
［7］Gandomi A H, Alavi A H. Krill herd: a new bio-inspired optimization algorithm ［J］. Communications in Nonlinear Science and Numerical Simulation, 2012, 17(12): 4831-4845.
［1］Li G Q, Niu P F, Zhang W P, et al. Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching-learning-based optimization ［J］. Chemometrics and Intelligent Laboratory Systems, 2013, 126(8): 11-20.
［2］Rao R V, Patel V. An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems［J］. Scientia Iranica, 2013, 20(3): 710-720.
［3］García J A M, Mena A J G. Optimal distributed generation location and size using a modified teaching-learning based optimization algorithm［J］. International journal of electrical power & energy systems,2013, 50(1): 65-75.
［4］牛培峰,王培坤,李国强,等.基于自由搜索算法和支持向量机的燃煤锅炉NOx 建模与优化［J］. 计量学报,2014,35(6):626-630.
［5］牛培峰,麻红波,李国强,等.基于GSA-SVM的循环流化床锅炉NOx排放特性模型［J］.计量学报,2013,34(6):602-606.
［6］牛培峰,王丘亚,马云鹏,等.基于量子自适应鸟群算法的锅炉NOx排放特性研究［J］. 计量学报, 2017,38(6):770-775.
［7］牛培峰,马云鹏,张京,等.基于相关向量机的电站锅炉NOx 燃烧优化［J］. 计量学报, 2016,37(2):626-630.
［8］Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks［C］//IEEE International Joint Conference on Neural Networks. Budapest,Hungary,2004:985-990.
［9］拓守恒, 雍龙泉, 邓方安. “教与学” 优化算法研究综述［J］. 计算机应用研究, 2013, 30(7): 1933-1938.
［10］冯艳红, 刘建芹, 贺毅朝. 基于混沌理论的动态种群萤火虫算法［J］. 计算机应用, 2013, 33(3): 191-196.
2018, Vol. 39 
